{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"font.sans-serif\"]=[\"SimHei\"]                          # 设置字体\n",
    "plt.rcParams[\"axes.unicode_minus\"]=False                            # 正常显示负号\n",
    "# -*- coding: utf-8 -*-\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               客运量    公路客运量    铁路客运量    航空客运量      旅客周转量    公路旅客运转量  \\\n",
      "时间                                                                    \n",
      "2023-02-01  3354.9   1840.8    919.2    594.9  1155668.5    39955.0   \n",
      "2023-03-01  3845.4   2132.8   1076.7    635.9  1252469.2    47446.8   \n",
      "2023-04-01  4360.3   2270.4   1346.7    743.2  1487065.7    55000.0   \n",
      "2023-05-01  4296.8   2279.0   1269.1    748.7  1523741.9    70168.5   \n",
      "2023-06-01  4276.8   2187.1   1308.1    781.6  1630294.8    69180.9   \n",
      "\n",
      "              铁路旅客运转量    航空旅客运转量     货物量    公路货物量    铁路货物量    航空货物量     货物周转量  \\\n",
      "时间                                                                              \n",
      "2023-02-01   112423.2  1003290.3  1314.4   1286.5     20.4      7.6  417624.4   \n",
      "2023-03-01   116966.7  1088055.7  1734.3   1700.5     24.4      9.4  498176.5   \n",
      "2023-04-01   141235.8  1290829.9  1825.7   1790.3     25.6      9.8  494225.3   \n",
      "2023-05-01   142778.3  1310795.1  1801.7   1755.2     35.7     10.9  501831.7   \n",
      "2023-06-01   141733.7  1419380.2  1738.6   1695.3     31.2     12.1  481750.0   \n",
      "\n",
      "               公路周转量     铁路周转量    航空周转量  \n",
      "时间                                       \n",
      "2023-02-01  168850.1  212844.6  35929.7  \n",
      "2023-03-01  220038.7  231938.4  46199.4  \n",
      "2023-04-01  232337.1  213777.6  48110.6  \n",
      "2023-05-01  229542.5  219431.7  52857.5  \n",
      "2023-06-01  223239.9  203281.1  55228.9  \n"
     ]
    }
   ],
   "source": [
    "# 数据导入，选择第三个sheet\n",
    "# 提取数据中的月份和客运量的数据作为数据集\n",
    "dataset = pd.read_excel('../data/全国铁路数据.xlsx', sheet_name = 2, header=0, parse_dates=[0],\n",
    "                        index_col=0)\n",
    "# 数据预览\n",
    "print(dataset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按照时间顺序绘制所有数据的折线图，采用不同颜色区分不同线\n",
    "plt.figure(figsize=(12, 6))\n",
    "for i in range(1, len(dataset.columns)):\n",
    "    plt.plot(dataset.index, dataset.iloc[:, i], label=dataset.columns[i])\n",
    "plt.legend()\n",
    "plt.title('全国铁路数据')\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('数量')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_line = dataset.index\n",
    "dataset = dataset.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建用于模型的数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "def creat_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    print(\"数据集长度：\",len(dataset))\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i: (i+look_back)] # 按照一个时间窗口来取数据\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i+look_back])\n",
    "    return np.array(dataX), np.array(dataY) # X是前look_back天的数据，Y是第look_back天的数据，用于训练的时候判断模型精度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = int(len(dataset)*0.8) # 2 8分\n",
    "test_size = len(dataset)-train_size\n",
    "train, test = dataset[0: train_size], dataset[train_size: len(dataset)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成训练和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集长度： 8\n",
      "数据集长度： 3\n"
     ]
    }
   ],
   "source": [
    "look_back = 1 # 表示考虑过去多久的数据\n",
    "trainX, trainY = creat_dataset(train, look_back)\n",
    "testX, testY = creat_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型定义"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义LSTM网络架构(用官方给出的库)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_12 (LSTM)              (None, None, 50)          10400     \n",
      "                                                                 \n",
      " lstm_13 (LSTM)              (None, None, 100)         60400     \n",
      "                                                                 \n",
      " lstm_14 (LSTM)              (None, None, 200)         240800    \n",
      "                                                                 \n",
      " lstm_15 (LSTM)              (None, 300)               601200    \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 300)               0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 100)               30100     \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 1)                 101       \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 1)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 943,001\n",
      "Trainable params: 943,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(units=50, input_shape=(None, 1), return_sequences=True)) # 50个神经元\n",
    "#model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units=100, return_sequences=True)) # 100个神经元\n",
    "#model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units=200, return_sequences=True)) # 200个神经元\n",
    "#model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(300, return_sequences=False)) # 300个神经元\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(100)) # 100个神经元全连接层，用于输出\n",
    "model.add(Dense(units=1)) # 1个神经元全连接层，用于输出，预测接下来一个时间点的股票价格\n",
    "\n",
    "model.add(Activation('relu')) # 激活函数\n",
    "start = time.time()\n",
    "model.compile(loss='mean_squared_error', optimizer='Adam')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练以及训练过程可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1/1 - 5s - loss: 0.4814 - val_loss: 0.4746 - 5s/epoch - 5s/step\n",
      "Epoch 2/50\n",
      "1/1 - 0s - loss: 0.4538 - val_loss: 0.4445 - 24ms/epoch - 24ms/step\n",
      "Epoch 3/50\n",
      "1/1 - 0s - loss: 0.4248 - val_loss: 0.4119 - 23ms/epoch - 23ms/step\n",
      "Epoch 4/50\n",
      "1/1 - 0s - loss: 0.3937 - val_loss: 0.3766 - 22ms/epoch - 22ms/step\n",
      "Epoch 5/50\n",
      "1/1 - 0s - loss: 0.3595 - val_loss: 0.3385 - 21ms/epoch - 21ms/step\n",
      "Epoch 6/50\n",
      "1/1 - 0s - loss: 0.3211 - val_loss: 0.2976 - 21ms/epoch - 21ms/step\n",
      "Epoch 7/50\n",
      "1/1 - 0s - loss: 0.2824 - val_loss: 0.2541 - 20ms/epoch - 20ms/step\n",
      "Epoch 8/50\n",
      "1/1 - 0s - loss: 0.2426 - val_loss: 0.2085 - 21ms/epoch - 21ms/step\n",
      "Epoch 9/50\n",
      "1/1 - 0s - loss: 0.1960 - val_loss: 0.1617 - 21ms/epoch - 21ms/step\n",
      "Epoch 10/50\n",
      "1/1 - 0s - loss: 0.1577 - val_loss: 0.1152 - 20ms/epoch - 20ms/step\n",
      "Epoch 11/50\n",
      "1/1 - 0s - loss: 0.1121 - val_loss: 0.0714 - 20ms/epoch - 20ms/step\n",
      "Epoch 12/50\n",
      "1/1 - 0s - loss: 0.0719 - val_loss: 0.0340 - 20ms/epoch - 20ms/step\n",
      "Epoch 13/50\n",
      "1/1 - 0s - loss: 0.0397 - val_loss: 0.0082 - 20ms/epoch - 20ms/step\n",
      "Epoch 14/50\n",
      "1/1 - 0s - loss: 0.0151 - val_loss: 1.7994e-04 - 21ms/epoch - 21ms/step\n",
      "Epoch 15/50\n",
      "1/1 - 0s - loss: 0.0069 - val_loss: 0.0139 - 21ms/epoch - 21ms/step\n",
      "Epoch 16/50\n",
      "1/1 - 0s - loss: 0.0268 - val_loss: 0.0371 - 20ms/epoch - 20ms/step\n",
      "Epoch 17/50\n",
      "1/1 - 0s - loss: 0.0489 - val_loss: 0.0511 - 21ms/epoch - 21ms/step\n",
      "Epoch 18/50\n",
      "1/1 - 0s - loss: 0.0641 - val_loss: 0.0492 - 22ms/epoch - 22ms/step\n",
      "Epoch 19/50\n",
      "1/1 - 0s - loss: 0.0630 - val_loss: 0.0372 - 21ms/epoch - 21ms/step\n",
      "Epoch 20/50\n",
      "1/1 - 0s - loss: 0.0544 - val_loss: 0.0223 - 22ms/epoch - 22ms/step\n",
      "Epoch 21/50\n",
      "1/1 - 0s - loss: 0.0343 - val_loss: 0.0103 - 24ms/epoch - 24ms/step\n",
      "Epoch 22/50\n",
      "1/1 - 0s - loss: 0.0231 - val_loss: 0.0028 - 21ms/epoch - 21ms/step\n",
      "Epoch 23/50\n",
      "1/1 - 0s - loss: 0.0109 - val_loss: 1.0923e-04 - 22ms/epoch - 22ms/step\n",
      "Epoch 24/50\n",
      "1/1 - 0s - loss: 0.0088 - val_loss: 7.2906e-04 - 21ms/epoch - 21ms/step\n",
      "Epoch 25/50\n",
      "1/1 - 0s - loss: 0.0105 - val_loss: 0.0033 - 21ms/epoch - 21ms/step\n",
      "Epoch 26/50\n",
      "1/1 - 0s - loss: 0.0108 - val_loss: 0.0068 - 21ms/epoch - 21ms/step\n",
      "Epoch 27/50\n",
      "1/1 - 0s - loss: 0.0121 - val_loss: 0.0101 - 21ms/epoch - 21ms/step\n",
      "Epoch 28/50\n",
      "1/1 - 0s - loss: 0.0161 - val_loss: 0.0128 - 20ms/epoch - 20ms/step\n",
      "Epoch 29/50\n",
      "1/1 - 0s - loss: 0.0178 - val_loss: 0.0146 - 20ms/epoch - 20ms/step\n",
      "Epoch 30/50\n",
      "1/1 - 0s - loss: 0.0207 - val_loss: 0.0152 - 20ms/epoch - 20ms/step\n",
      "Epoch 31/50\n",
      "1/1 - 0s - loss: 0.0191 - val_loss: 0.0148 - 20ms/epoch - 20ms/step\n",
      "Epoch 32/50\n",
      "1/1 - 0s - loss: 0.0205 - val_loss: 0.0135 - 20ms/epoch - 20ms/step\n",
      "Epoch 33/50\n",
      "1/1 - 0s - loss: 0.0185 - val_loss: 0.0115 - 20ms/epoch - 20ms/step\n",
      "Epoch 34/50\n",
      "1/1 - 0s - loss: 0.0155 - val_loss: 0.0092 - 19ms/epoch - 19ms/step\n",
      "Epoch 35/50\n",
      "1/1 - 0s - loss: 0.0134 - val_loss: 0.0069 - 20ms/epoch - 20ms/step\n",
      "Epoch 36/50\n",
      "1/1 - 0s - loss: 0.0111 - val_loss: 0.0046 - 21ms/epoch - 21ms/step\n",
      "Epoch 37/50\n",
      "1/1 - 0s - loss: 0.0128 - val_loss: 0.0027 - 20ms/epoch - 20ms/step\n",
      "Epoch 38/50\n",
      "1/1 - 0s - loss: 0.0104 - val_loss: 0.0013 - 21ms/epoch - 21ms/step\n",
      "Epoch 39/50\n",
      "1/1 - 0s - loss: 0.0087 - val_loss: 3.7045e-04 - 21ms/epoch - 21ms/step\n",
      "Epoch 40/50\n",
      "1/1 - 0s - loss: 0.0094 - val_loss: 1.7490e-05 - 21ms/epoch - 21ms/step\n",
      "Epoch 41/50\n",
      "1/1 - 0s - loss: 0.0084 - val_loss: 8.5351e-05 - 22ms/epoch - 22ms/step\n",
      "Epoch 42/50\n",
      "1/1 - 0s - loss: 0.0105 - val_loss: 4.0020e-04 - 21ms/epoch - 21ms/step\n",
      "Epoch 43/50\n",
      "1/1 - 0s - loss: 0.0088 - val_loss: 8.0379e-04 - 20ms/epoch - 20ms/step\n",
      "Epoch 44/50\n",
      "1/1 - 0s - loss: 0.0113 - val_loss: 0.0011 - 21ms/epoch - 21ms/step\n",
      "Epoch 45/50\n",
      "1/1 - 0s - loss: 0.0113 - val_loss: 0.0012 - 20ms/epoch - 20ms/step\n",
      "Epoch 46/50\n",
      "1/1 - 0s - loss: 0.0129 - val_loss: 0.0011 - 20ms/epoch - 20ms/step\n",
      "Epoch 47/50\n",
      "1/1 - 0s - loss: 0.0103 - val_loss: 7.5384e-04 - 20ms/epoch - 20ms/step\n",
      "Epoch 48/50\n",
      "1/1 - 0s - loss: 0.0102 - val_loss: 4.2728e-04 - 20ms/epoch - 20ms/step\n",
      "Epoch 49/50\n",
      "1/1 - 0s - loss: 0.0095 - val_loss: 1.6738e-04 - 20ms/epoch - 20ms/step\n",
      "Epoch 50/50\n",
      "1/1 - 0s - loss: 0.0109 - val_loss: 1.2550e-05 - 21ms/epoch - 21ms/step\n",
      "compilation time: 6.204268455505371\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(trainX, trainY, batch_size=64, epochs=50, \n",
    "                    validation_split=0.1, verbose=2)\n",
    "print('compilation time:', time.time()-start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 893ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n"
     ]
    }
   ],
   "source": [
    "trainPredict = model.predict(trainX)\n",
    "testPredict = model.predict(testX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据反归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform(trainY)\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY = scaler.inverse_transform(testY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 画图展示结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainPredictPlot = np.empty_like(dataset)\n",
    "trainPredictPlot[:] = np.nan\n",
    "trainPredictPlot = np.reshape(trainPredictPlot, (dataset.shape[0], 1))\n",
    "trainPredictPlot[look_back: len(trainPredict)+look_back, :] = trainPredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "testPredictPlot = np.empty_like(dataset)\n",
    "testPredictPlot[:] = np.nan\n",
    "testPredictPlot = np.reshape(testPredictPlot, (dataset.shape[0], 1))\n",
    "testPredictPlot[len(trainPredict)+(look_back*2)+1: len(dataset)-1, :] = testPredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(20, 15))\n",
    "plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.xticks(range(0, len(time_line), 100), time_line[::100])\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('price')\n",
    "plt.xlabel('时间')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=(20, 15))\n",
    "plt.plot(np.arange(train_size+1, len(dataset)+1, 1), scaler.inverse_transform(dataset)[train_size:], label='dataset')\n",
    "plt.plot(testPredictPlot, 'g', label='test')\n",
    "plt.ylabel('price')\n",
    "plt.xlabel('时间')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
 ],
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